Predicting Surface Urban Heat Island in Meihekou City, China: A Combination Method of Monte Carlo and Random Forest  

作　　者：ZHANG Yao LIU Jiafu WEN Zhuyun 

机构地区：[1]College of Tourism and Geographical Sciences,Jilin Normal University,Siping 136000,China

出　　处：《Chinese Geographical Science》2021年第4期659-670,共12页中国地理科学（英文版）

基　　金：Under the auspices of National Natural Science Foundation of China(No.41977411,41771383);Technology Research Project of the Education Department of Jilin Province(No.JJKH20210445KJ)。

摘　　要：Given the rapid urbanization worldwide, Urban Heat Island(UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat island(SUHI) in China’s Meihekou City, a combination method of Monte Carlo and Random Forest Regression(MC-RFR) is developed to construct the relationship between landscape pattern indices and Land Surface Temperature(LST). In this method, Monte Carlo acceptance-rejection sampling was added to the bootstrap layer of RFR to ensure the sensitivity of RFR to outliners of SUHI effect. The SHUI in 2030 was predicted by using this MC-RFR and the modeled future landscape pattern by Cellular Automata and Markov combination model(CA-Markov). Results reveal that forestland can greatly alleviate the impact of SUHI effect, while reasonable construction of urban land can also slow down the rising trend of SUHI. MC-RFR performs better for characterizing the relationship between landscape pattern and LST than single RFR or Linear Regression model. By 2030, the overall SUHI effect of Meihekou will be greatly enhanced, and the center of urban development will gradually shift to the central and western regions of the city. We suggest that urban designer and managers should concentrate vegetation and disperse built-up land to weaken the SUHI in the construction of new urban areas for its sustainability.

关 键 词：Monte Carlo and Random Forest Regression(MC-RFR) landscape pattern surface heat island effect Cellular Automata and Markov combination model(CA-Markov) 

分 类 号：X16[环境科学与工程—环境科学]